Use of anti-solvent to enhance thermoelectric response of hybrid-halide perovskite thin films

Hybrid halide perovskite has been recently focused on thermoelectric energy harvesting due to the cost-effective fabrication approach and ultra-low thermal conductivity. To achieve high performance, tuning of electrical conductivity is a key parameter that is influenced by grain boundary scattering and charge carrier density. The fabrication process allows tuning these parameters. We report the use of anti-solvent to enhance the thermoelectric performance of lead-free hybrid halide perovskite, CH3NH3SnI3, thin films. Thin films with anti-solvent show higher connectivity in grains and higher Sn+4 oxidation states which results in enhancing the value of electrical conductivity. Thin films were prepared by a cost-effective wet process. Structural and chemical characterizations were performed using x-ray diffraction, scanning electron microscope, and x-ray photoelectron spectroscopy. The value of electrical conductivity and the Seebeck coefficient were measured near room temperature. The high value of power factor (1.55 µW/m.K2 at 320 K) was achieved for thin films treated with anti-solvent.

Software Architecture Modeling of AUTOSAR-Based Multi-Core Mixed-Critical Electric Powertrain Controller

In this paper, we present a transition journey of automotive software architecture design from using legacy approaches and toolchains to employing new modeling capabilities in the recent releases of Matlab/Simulink (M/S). We present the seamless approach that we have employed for the software architecture modeling of a mixed-critical electric powertrain controller which runs on a multi-core hardware platform. With our approach, we can achieve bidirectional traceability along with a powerful authoring process, implement a detailed model-based software architecture design of AUTOSAR system including a detailed data dictionary, and carry out umpteen number of proof-of-concept studies, what-if scenario simulations and performance tuning of safety software. In this context, we discuss an industrial case study employing valuable lessons learned, our experience reports providing novel insights and best practices followed.

Reset and prescribed performance approach to spacecraft attitude regulation

Purpose This paper aims to investigate the attitude regulation for spacecraft in the presence of time-varying inertia uncertainty and exogenous disturbances. Design/methodology/approach The high gain approaches are typically used in existing researches for theoretical advantages, bringing better performance but sensitive to parameter selection, making the controller conservative. A reset-control policy is presented to achieve the spacecraft attitude control with easy control parameter tuning. Findings The reset-control policy guarantees satisfying control performance despite using performance tuning function and saturation function besides reducing the conservativeness of the controller, thus reducing the effort in tuning control parameters. Originality/value Notably, the adaptive function owns a reset mechanism, which is reset to a preset condition when the controlled variable crosses zero. The mathematical analysis also shows the system trajectory can converge to a set centered at the origin.

A machine learning based approach to clinopyroxene thermobarometry: model optimisation and distribution for use in Earth Sciences

Thermobarometry is a fundamental tool to quantitatively interrogate magma plumbing systems and broaden our appreciation of volcanic processes. Developments in random forest-based machine learning lend themselves to a more data-driven approach to clinopyroxene thermobarometry. This can include allowing users to access and filter large experimental datasets that can be tailored to individual applications in Earth Sciences. Here we present a methodological assessment of random forest thermobarometry, using the R freeware package “extraTrees”, by investigating the model performance, tuning hyperparameters, and evaluating different methods for calculating uncertainties. We determine that deviating from the default hyperparameters used in the “extraTrees” package results in little difference in overall model performance (<0.2 kbar and <3 ⁰C difference in mean SEE). However, accuracy is greatly affected by how the final pressure or temperature (PT) value from the voting distribution of trees in the random forest is selected (mean, median or mode). This thus far has been unapproached in machine learning thermobarometry. Using the mean value leads to a higher residual between experimental and predicted PT, whereas using median values produces smaller residuals. Additionally, this work provides two comprehensive R scripts for users to apply the random forest methodology to natural datasets. The first script permits modification and filtering of the model calibration dataset. The second script contains pre-made models in which users can rapidly input their data to recover pressure and temperature estimates. These scripts are open source and can be accessed at https://github.com/corinjorgenson/RandomForest-cpx-thermobarometer.